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Gain-field Encoding
Gain field encoding is a hypothesis about the internal storage and processing of limb motion in the brain. In the motor areas of the brain, there are neurons which collectively have the ability to store information regarding both limb positioning and velocity in relation to both the body (intrinsic) and the individual's external environment (extrinsic). The input from these neurons is taken multiplicatively, forming what is referred to as a gain field. The gain field works as a collection of internal models off of which the body can base its movements. The process of encoding and recalling these models is the basis of muscle memory. Physiology Neuron involved in gain field encoding work multiplicatively, taking the input from several together to form the gain field. It is this process that allows the complexity of motor control. Instead of simply encoding the motion of the limb in which a specific motion is desired, the multiplicative nature of the gain field ensures that the ...
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Motor Cortex
The motor cortex is the region of the cerebral cortex believed to be involved in the planning, control, and execution of voluntary movements. The motor cortex is an area of the frontal lobe located in the posterior precentral gyrus immediately anterior to the central sulcus. Components of the motor cortex The motor cortex can be divided into three areas: 1. The primary motor cortex is the main contributor to generating neural impulses that pass down to the spinal cord and control the execution of movement. However, some of the other motor areas in the brain also play a role in this function. It is located on the anterior paracentral lobule on the medial surface. 2. The premotor cortex is responsible for some aspects of motor control, possibly including the preparation for movement, the sensory guidance of movement, the spatial guidance of reaching, or the direct control of some movements with an emphasis on control of proximal and trunk muscles of the body. Located anter ...
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Hemispatial Neglect
Hemispatial neglect is a neuropsychological condition in which, after damage to one hemisphere of the brain (e.g. after a stroke), a deficit in attention and awareness towards the side of space opposite brain damage (contralesional space) is observed. It is defined by the inability of a person to process and perceive stimuli towards the contralesional side of the body or environment. Hemispatial neglect is very commonly contralateral to the damaged hemisphere, but instances of ipsilesional neglect (on the same side as the lesion) have been reported. Presentation Hemispatial neglect results most commonly from strokes and brain unilateral injury to the right cerebral hemisphere, with rates in the critical stage of up to 80% causing visual neglect of the left-hand side of space. Neglect is often produced by massive strokes in the middle cerebral artery region and is variegated, so that most sufferers do not exhibit all of the syndrome's traits. Right-sided spatial neglect is rare ...
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Recurrent Neural Network
A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. Recurrent neural networks are theoretically Turing complete and can run arbitrary programs to process arbitrary sequences of inputs. The term "recurrent neural network" is used to refer to the class of networks with an infinite impulse response, whereas "convolutional neural network" refers to the class of finite impulse response. Both classes of networks exhibit temporal dynamic behavior. A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replace ...
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Frame Of Reference
In physics and astronomy, a frame of reference (or reference frame) is an abstract coordinate system whose origin, orientation, and scale are specified by a set of reference points― geometric points whose position is identified both mathematically (with numerical coordinate values) and physically (signaled by conventional markers). For ''n'' dimensions, reference points are sufficient to fully define a reference frame. Using rectangular Cartesian coordinates, a reference frame may be defined with a reference point at the origin and a reference point at one unit distance along each of the ''n'' coordinate axes. In Einsteinian relativity, reference frames are used to specify the relationship between a moving observer and the phenomenon under observation. In this context, the term often becomes observational frame of reference (or observational reference frame), which implies that the observer is at rest in the frame, although not necessarily located at its origin. A rela ...
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Directed Cycle
Director may refer to: Literature * ''Director'' (magazine), a British magazine * ''The Director'' (novel), a 1971 novel by Henry Denker * ''The Director'' (play), a 2000 play by Nancy Hasty Music * Director (band), an Irish rock band * ''Director'' (Avant album) (2006) * ''Director'' (Yonatan Gat album) Occupations and positions Arts and design * Animation director * Artistic director * Creative director * Design director * Film director * Music director * Music video director * Sports director * Television director * Theatre director Positions in other fields * Director (business), a senior level management position * Director (colonial), head of chartered company's colonial administration in a territory * Director (education), head of a university or other educational body * Company director * Cruise director * Executive director * Finance director or chief financial officer * Funeral director * Managing director * Non-executive director * Technical director * T ...
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Recurrent Neural Network
A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. Recurrent neural networks are theoretically Turing complete and can run arbitrary programs to process arbitrary sequences of inputs. The term "recurrent neural network" is used to refer to the class of networks with an infinite impulse response, whereas "convolutional neural network" refers to the class of finite impulse response. Both classes of networks exhibit temporal dynamic behavior. A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replace ...
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Retinal Ganglion Cell
A retinal ganglion cell (RGC) is a type of neuron located near the inner surface (the ganglion cell layer) of the retina of the eye. It receives visual information from photoreceptors via two intermediate neuron types: bipolar cells and retina amacrine cells. Retina amacrine cells, particularly narrow field cells, are important for creating functional subunits within the ganglion cell layer and making it so that ganglion cells can observe a small dot moving a small distance. Retinal ganglion cells collectively transmit image-forming and non-image forming visual information from the retina in the form of action potential to several regions in the thalamus, hypothalamus, and mesencephalon, or midbrain. Retinal ganglion cells vary significantly in terms of their size, connections, and responses to visual stimulation but they all share the defining property of having a long axon that extends into the brain. These axons form the optic nerve, optic chiasm, and optic tract. ...
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Cognition
Cognition refers to "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses". It encompasses all aspects of intellectual functions and processes such as: perception, attention, thought, intelligence, the formation of knowledge, memory and working memory, judgment and evaluation, reasoning and computation, problem solving and decision making, comprehension and production of language. Imagination is also a cognitive process, it is considered as such because it involves thinking about possibilities. Cognitive processes use existing knowledge and discover new knowledge. Cognitive processes are analyzed from different perspectives within different contexts, notably in the fields of linguistics, musicology, anesthesia, neuroscience, psychiatry, psychology, education, philosophy, anthropology, biology, systemics, logic, and computer science. These and other approaches to the analysis of cognition (such as embodie ...
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Spatial Memory
In cognitive psychology and neuroscience, spatial memory is a form of memory responsible for the recording and recovery of information needed to plan a course to a location and to recall the location of an object or the occurrence of an event. Spatial memory is necessary for orientation in space. Spatial memory can also be divided into egocentric and allocentric spatial memory. A person's spatial memory is required to navigate around a familiar city. A rat's spatial memory is needed to learn the location of food at the end of a maze. In both humans and animals, spatial memories are summarized as a cognitive map. Spatial memory has representations within working, short-term memory and long-term memory. Research indicates that there are specific areas of the brain associated with spatial memory. Many methods are used for measuring spatial memory in children, adults, and animals. Short-term spatial memory Short-term memory (STM) can be described as a system allowing one to tempor ...
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Coordinate System
In geometry, a coordinate system is a system that uses one or more numbers, or coordinates, to uniquely determine the position of the points or other geometric elements on a manifold such as Euclidean space. The order of the coordinates is significant, and they are sometimes identified by their position in an ordered tuple and sometimes by a letter, as in "the ''x''-coordinate". The coordinates are taken to be real numbers in elementary mathematics, but may be complex numbers or elements of a more abstract system such as a commutative ring. The use of a coordinate system allows problems in geometry to be translated into problems about numbers and ''vice versa''; this is the basis of analytic geometry. Common coordinate systems Number line The simplest example of a coordinate system is the identification of points on a line with real numbers using the '' number line''. In this system, an arbitrary point ''O'' (the ''origin'') is chosen on a given line. The coordinate of ...
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Visual Field
The visual field is the "spatial array of visual sensations available to observation in introspectionist psychological experiments". Or simply, visual field can be defined as the entire area that can be seen when an eye is fixed straight at a point. The equivalent concept for optical instruments and image sensors is the field of view (FOV). In optometry, ophthalmology, and neurology, a visual field test is used to determine whether the visual field is affected by diseases that cause local scotoma or a more extensive loss of vision or a reduction in sensitivity (increase in threshold). Normal limits The normal (monocular) human visual field extends to approximately 60 degrees nasally (toward the nose, or inward) from the vertical meridian in each eye, to 107 degrees temporally (away from the nose, or outwards) from the vertical meridian, and approximately 70 degrees above and 80 below the horizontal meridian. The binocular visual field is the superimposition of the two mo ...
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Artificial Neural Network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, ...
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